Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation
Author:
Affiliation:
1. Instituto de Matemática Aplicada del Litoral, IMAL, UNL, CONICET, Santa Fe, Argentina
2. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL, CONICET, Santa Fe, Argentina
Funder
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) through PUE-IMAL and PIP-CONICET
Universidad Nacional del Litoral (UNL), through CAI+D-UNL 2020
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/7333/10398877/10565958.pdf?arnumber=10565958
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1. Progress in Brain Computer Interface: Challenges and Opportunities
2. Towards identifying optimal biased feedback for various user states and traits in motor imagery BCI
3. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke
4. Deep learning-based electroencephalography analysis: a systematic review
5. Generating artificial EEG signals to reduce BCI calibration time;Lotte
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